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- Martingale difference (3)
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Articles 1 - 30 of 37
Full-Text Articles in Social and Behavioral Sciences
Semiparametric Maximum Likelihood Inference For Nonignorable Nonresponse With Callbacks, Zhong Guan, Denis H. Y. Leung, Jing Qin
Semiparametric Maximum Likelihood Inference For Nonignorable Nonresponse With Callbacks, Zhong Guan, Denis H. Y. Leung, Jing Qin
Research Collection School Of Economics
We model the nonresponse probabilities as logistic functions ofthe outcome variable and other covariates in the survey sampling study withcallback. The identification aspect of this callback model is investigated. Semiparametricmaximum likelihood estimators of the parameters in the responseprobabilities are proposed and studied. As a result, an efficient estimator ofthe mean of the outcome variable is constructed using the estimated responseprobabilities. Moreover, if a regression model for conditional mean of the outcomevariable given some covariate is available, then we can obtain an evenmore efficient estimate of the mean of the outcome variable by fitting the regressionmodel using an adjusted least squares …
Root-N Consistency Of Intercept Estimators In A Binary Response Model Under Tail Restrictions, Lili Tan, Yichong Zhang
Root-N Consistency Of Intercept Estimators In A Binary Response Model Under Tail Restrictions, Lili Tan, Yichong Zhang
Research Collection School Of Economics
The intercept of the binary response model is irregularly identified when the supports of both the special regressor V and the error term ε are the whole real line. This leads to the estimator of the intercept having potentially a slower than √n convergence rate, which can result in a large estimation error in practice. This paper imposes addition tail restrictions which guarantee the regular identification of the intercept and thus the √n-consistency of its estimator. We then propose an estimator that achieves the √n rate. Finally, we extend our tail restrictions to a full-blown model with endogenous regressors.
Mild-Explosive And Local-To-Mild-Explosive Autoregressions With Serially Correlated Errors, Yiu Lim Lui, Weilin Xiao, Jun Yu
Mild-Explosive And Local-To-Mild-Explosive Autoregressions With Serially Correlated Errors, Yiu Lim Lui, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper firstly extends the results of Phillips and Magdalinos (2007a) by allowing for anti-persistent errors in mildly explosive autoregressive models. It is shown that the Cauchy asymptotic theory remains valid for the least squares (LS) estimator. The paper then extends the results of Phillips, Magdalinos and Giraitis (2010) by allowing for serially correlated errors of various forms in local-to-mild-explosive autoregressive models. It is shown that the result of smooth transition in the limit theory between local-to-unity and mild-explosiveness remains valid for the LS estimator. Finally, the limit theory for autoregression with intercept is developed.
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Correlation Matrix Approach, Yingjie Dong, Yiu Kuen Tse
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Correlation Matrix Approach, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We propose a factor correlation matrix approach to forecast large covariance matrix of asset returns using high-frequency data. We apply shrinkage method to estimate large correlation matrix and adopt principal component method to model the underlying latent factors. A vector autoregressive model is used to forecast the latent factors and hence the large correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. We conduct Monte Carlo studies to compare the finite sample performance of several methods of forecasting large covariance matrix. Our proposed …
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Xin Zheng, Yichong Zhang
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Xin Zheng, Yichong Zhang
Research Collection School Of Economics
This paper studies the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose three estimation methods: (1) the simple quantile regression (QR), (2) the QR with strata fixed effects, and (3) the inverse propensity score weighted QR. For the three estimators, we derive their asymptotic distributions uniformly over a set of quantile indexes and show that the estimator obtained from inverse propensity score weighted QR weakly dominates the other two in terms of efficiency, for a wide range of randomization schemes. For inference, we show that the weighted bootstrap tends to be conservative for methods (1) …
The Grid Bootstrap For Continuous Time Models, Yiu Lim Lui, Weilin Xiao, Jun Yu
The Grid Bootstrap For Continuous Time Models, Yiu Lim Lui, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper considers the grid bootstrap for constructing confidence intervals for the persistence parameter in a class of continuous time models driven by a Levy process. Its asymptotic validity is established by assuming the sampling interval (h) shrinks to zero. Its improvement over the in-fill asymptotic theory is achieved by expanding the coefficient-based statistic around its in fill asymptotic distribution which is non-pivotal and depends on the initial condition. Monte Carlo studies show that the gird bootstrap method performs better than the in-fill asymptotic theory and much better than the long-span theory. Empirical applications to U.S. interest rate data highlight …
Threshold Regression Asymptotics: From The Compound Poisson Process To Two-Sided Brownian Motion, Ping Yu, Peter C. B. Phillips
Threshold Regression Asymptotics: From The Compound Poisson Process To Two-Sided Brownian Motion, Ping Yu, Peter C. B. Phillips
Research Collection School Of Economics
The asymptotic distribution of the least squares estimator in threshold regression is expressed in terms of a compound Poisson process when the threshold effect is fixed and as a functional of two-sided Brownian motion when the threshold effect shrinks to zero. This paper explains the relationship between this dual limit theory by showing how the asymptotic forms are linked in terms of joint and sequential limits. In one case, joint asymptotics apply when both the sample size diverges and the threshold effect shrinks to zero, whereas sequential asymptotics operate in the other case in which the sample size diverges first …
Change Detection And The Causal Impact Of The Yield Curve, Shuping Shi, Peter C. B. Phillips, Stan Hurn
Change Detection And The Causal Impact Of The Yield Curve, Shuping Shi, Peter C. B. Phillips, Stan Hurn
Research Collection School Of Economics
Causal relationships in econometrics are typically based on the concept of predictability and are established by testing Granger causality. Such relationships are susceptible to change, especially during times of financial turbulence, making the real-time detection of instability an important practical issue. This article develops a test for detecting changes in causal relationships based on a recursive evolving window, which is analogous to a procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity and conditional heteroskedasticity of an …
Specification Tests Based On Mcmc Output, Yong Li, Jun Yu, Tao Zeng
Specification Tests Based On Mcmc Output, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
Two test statistics are proposed to determine model specification after a model is estimated by an MCMC method. The first test is the MCMC version of IOSA test and its asymptotic null distribution is normal. The second test is motivated from the power enhancement technique of Fan et al. (2015). It combines a component (J1) that tests a null point hypothesis in an expanded model and a power enhancement component (J0) obtained from the first test. It is shown that J0 converges to zero when the null model is correctly specified and diverges when the null model is misspecified. Also …
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Research Collection School Of Economics
In this paper we study the estimation of a large dimensional factor model when the factor loadingsexhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the third step, we devise a grid search method to estimate the location of the break on each identified interval. …
News Co-Occurrence, Attention Spillover, And Return Predictability, Li Guo, Lin Peng, Yubo Tao, Jun Tu
News Co-Occurrence, Attention Spillover, And Return Predictability, Li Guo, Lin Peng, Yubo Tao, Jun Tu
Research Collection School Of Economics
We examine the effect of investor attention spillover on stock return predictability. Using a novel measure, the News Network Triggered Attention index (NNTA), we find that NNTA negatively predicts market returns with a monthly in(out)-of-sample R-square of 5.97% (5.80%). In the cross-section, a long-short portfolio based on news co-occurrence generates a significant monthly alpha of 68 basis points. The results are robust to the inclusion of alternative attention proxies, sentiment measures, other news- and information-based predictors, across recession and expansion periods. We further validate the attention spillover effect by showing that news co-mentioning leads to greater increases in Google and …
Identifying Latent Grouped Patterns In Cointegrated Panels, Wenxin Huang, Sainan Jin, Liangjun Su
Identifying Latent Grouped Patterns In Cointegrated Panels, Wenxin Huang, Sainan Jin, Liangjun Su
Research Collection School Of Economics
We consider a panel cointegration model with latent group structures that allows for heterogeneous long-run relationships across groups. We extend Su, Shi, and Phillips’ (2016) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the stationary regressors to diverge with the sample size. We show that we can identify the individuals’ group membership and estimate the group-specific long-run cointegrated relationships simultaneously. We demonstrate the desirable property of uniform classification consistency and the oracle properties of both the C-Lasso …
Volume, Volatility, And Public News Announcements, Tim Bollerslev, Jia Li, Yuan Xue
Volume, Volatility, And Public News Announcements, Tim Bollerslev, Jia Li, Yuan Xue
Research Collection School Of Economics
We provide new empirical evidence for the way in which financial markets process information. Our results rely critically on high-frequency intraday price and volume data for the S&P 500 equity portfolio and U.S. Treasury bonds, along with new econometric techniques, for making inference on the relationship between trading intensity and spot volatility around public news announcements. Consistent with the predictions derived from a theoretical model in which investors agree to disagree, our estimates for the intraday volume-volatility elasticity around important news announcements are systematically below unity. Our elasticity estimates also decrease significantly with measures of disagreements in beliefs, economic uncertainty, …
Identifying Latent Grouped Patterns In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Gaosheng Ju
Identifying Latent Grouped Patterns In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Gaosheng Ju
Research Collection School Of Economics
We consider the estimation of latent grouped patterns in dynamic panel data models with interactive fixed effects. We assume that the individual slope coefficients are homogeneous within a group and heterogeneous across groups but each individual’s group membership is unknown to the researcher. We consider penalized principal component (PPC) estimation by extending the penalized-profile-likelihood-based C-Lasso of Su, Shi, and Phillips (2016) to panel data models with cross section dependence. Given the correct number of groups, we show that the C-Lasso can achieve simultaneous classification and estimation in a single step and exhibit the desirable property of uniform classification consistency. The …
A Frequentist Approach To Bayesian Asymptotics, Tingting Cheng, Jiti Gao, Peter C. B. Phillips
A Frequentist Approach To Bayesian Asymptotics, Tingting Cheng, Jiti Gao, Peter C. B. Phillips
Research Collection School Of Economics
Ergodic theorem shows that ergodic averages of the posterior draws converge in probability to the posterior mean under the stationarity assumption. The literature also shows that the posterior distribution is asymptotically normal when the sample size of the original data considered goes to infinity. To the best of our knowledge, there is little discussion on the large sample behaviour of the posterior mean. In this paper, we aim to fill this gap. In particular, we extend the posterior mean idea to the conditional mean case, which is conditioning on a given vector of summary statistics of the original data. We …
Comment On: Limit Of Random Measures Associated With The Increments Of A Brownian Semimartingale, Jia Li, Dacheng Xiu
Comment On: Limit Of Random Measures Associated With The Increments Of A Brownian Semimartingale, Jia Li, Dacheng Xiu
Research Collection School Of Economics
We thank the Editors’ invitation for the opportunity of contributing to this special issue as a celebration of Professor Jean Jacod’s seminal work originally written in 1994 (Jacod, 1994). This paper established general limit theorems for integrated volatility functionals, and provided theoretical tools that eventually changed the landscape of theoretical research concerning high-frequency data. This impact is also largely due to Professor Jacod’s continuous contribution to a broad variety of challenging issues in the area of high-frequency financial econometrics, including volatility estimation, jumps, and microstructure noise, as well as a large body of mathematical results collected in Jacod and Shiryaev …
Homogeneity Pursuit In Panel Data Models: Theory And Application, Wuyi Wang, Peter C. B. Phillips, Liangjun Su
Homogeneity Pursuit In Panel Data Models: Theory And Application, Wuyi Wang, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel-CARDS. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate the performance and corroborate the asymptotic theory in several practical design settings. The empirical application reveals the heterogeneous grouping effect of income on democracy.
Aging Suppresses Skin-Derived Circulating Sdf1 To Promote Full-Thickness Tissue Regeneration, Mailyn A. Nishiguchi, Casey A. Spencer, Denis H. Y. Leung, Thomas H. Leung
Aging Suppresses Skin-Derived Circulating Sdf1 To Promote Full-Thickness Tissue Regeneration, Mailyn A. Nishiguchi, Casey A. Spencer, Denis H. Y. Leung, Thomas H. Leung
Research Collection School Of Economics
Physicians have observed that surgical wounds in the elderly heal with thinner scars than wounds in young patients. Understanding this phenomenon may reveal strategies for promoting scarless wound repair. We show that full-thickness skin wounds in aged but not young mice fully regenerate. Exposure of aged animals to blood from young mice by parabiosis counteracts this regenerative capacity. The secreted factor, stromal-derived factor 1 (SDF1), is expressed at higher levels in wounded skin of young mice. Genetic deletion of SDF1 in young skin enhanced tissue regeneration. In aged mice, enhancer of zeste homolog 2 (EZH2) and histone H3 lysine 27 …
Editorial For The Special Issue Entitled: New Advances In Spatial Econometrics: Interactions Matter, Nicolas Debarsy, Zhenlin Yang
Editorial For The Special Issue Entitled: New Advances In Spatial Econometrics: Interactions Matter, Nicolas Debarsy, Zhenlin Yang
Research Collection School Of Economics
This Regional Science and Urban Economics special issue collects together a subset of contributions presented in the 15th edition of the International Workshop in Spatial Econometrics and Statistics, which was organized by the Department of Economics of the University of Orléans (Laboratoire d’Economie d’Orléans – UMR CNRS 7322) on May 26-27 2016
Spatial Dynamic Panel Data Models With Correlated Random Effects, Liyao Li, Zhenlin Yang
Spatial Dynamic Panel Data Models With Correlated Random Effects, Liyao Li, Zhenlin Yang
Research Collection School Of Economics
In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by adjusting the conditional quasi-scores given the initial observations, leading to M-estimators that are consistent, asymptotically normal, and free from the initial conditions except the process starting time. By decomposing the estimating functions into sums …
Financial Bubble Implosion And Reverse Regression, Peter C. B. Phillips, Shu-Ping Shi
Financial Bubble Implosion And Reverse Regression, Peter C. B. Phillips, Shu-Ping Shi
Research Collection School Of Economics
Expansion and collapse are two key features of a financial asset bubble. Bubble expansionmay be modeled using a mildly explosive process. Bubble implosion may take several differentforms depending on the nature of the collapse and therefore requires some flexibility in modeling.This paper first strengthens the theoretical foundation of the real time bubble monitoringstrategy proposed in Phillips, Shi and Yu (2015a,b, PSY) by developing analytics and studyingthe performance characteristics of the testing algorithm under alternative forms of bubbleimplosion which capture various return paths to market normalcy. Second, we propose a newreverse sample use of the PSY procedure for detecting crises and …
Unified M-Estimation Of Fixed-Effects Spatial Dynamic Models With Short Panels, Zhenlin Yang
Unified M-Estimation Of Fixed-Effects Spatial Dynamic Models With Short Panels, Zhenlin Yang
Research Collection School Of Economics
It is well known that quasi maximum likelihood (QML) estimation of dynamic panel data (DPD) models with short panels depends on the assumptions on the initial values, and a wrong treatment of them will result in inconsistency and serious bias. The same issues apply to spatial DPD (SDPD) models with short panels. In this paper, a unified Mestimation method is proposed for estimating the fixed-effects SDPD models containing three major types of spatial effects, namely spatial lag, spatial error and space-time lag. The method is free from the specification of the distribution of the initial observations and robust against nonnormality …
Bootstrap Lm Tests For Higher-Order Spatial Effects In Spatial Linear Regression Models, Zhenlin Yang
Bootstrap Lm Tests For Higher-Order Spatial Effects In Spatial Linear Regression Models, Zhenlin Yang
Research Collection School Of Economics
This paper first extends the methodology of Yang (J Econom 185:33-59, 2015) to allow for non-normality and/or unknown heteroskedasticity in obtaining asymptotically refined critical values for the LM-type tests through bootstrap. Bootstrap refinements in critical values require the LM test statistics to be asymptotically pivotal under the null hypothesis, and for this we provide a set of general methods for constructing LM and robust LM tests. We then give detailed treatments for two general higher-order spatial linear regression models: namely the model and the model, by providing a complete set of non-normality robust LM and bootstrap LM tests for higher-order …
New Distribution Theory For The Estimation Of Structural Break Point In Mean, Liang Jiang, Xiaohu Wang, Jun Yu
New Distribution Theory For The Estimation Of Structural Break Point In Mean, Liang Jiang, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
Based on the Girsanov theorem, this paper obtains the exact distribution of the maximum likelihood estimator of structural break point in a continuous time model. The exact distribution is asymmetric and tri-modal, indicating that the estimator is biased. These two properties are also found in the finite sample distribution of the least squares (LS) estimator of structural break point in the discrete time model, suggesting the classical long-span asymptotic theory is inadequate. The paper then builds a continuous time approximation to the discrete time model and develops an in-fill asymptotic theory for the LS estimator. The in-fill asymptotic distribution is …
Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi H. Baltagi, Alain Pirotte, Zhenlin Yang
Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi H. Baltagi, Alain Pirotte, Zhenlin Yang
Research Collection School Of Economics
We propose tests for homoskedasticity in spatial econometric models, based on joint or concentrated score functions and an Outer-Product-of-Martingale-Difference (OPMD) estimate of the variance of the joint or concentrated score functions. Versions of these tests robust against non-normality are also given. Asymptotic properties of the proposed tests are formally examined using a cross-section model and a panel model with fixed effects. Monte Carlo results show that the proposed tests based on the concentrated score function have good finite sample properties. Finally, the generality of the proposed approach in constructing tests for homoskedasticity is further demonstrated using a spatial dynamic panel …
Asymptotics And Bootstrap For Random-Effects Panel Data Transformation Models, Liangjun Su, Zhenlin Yang
Asymptotics And Bootstrap For Random-Effects Panel Data Transformation Models, Liangjun Su, Zhenlin Yang
Research Collection School Of Economics
This article investigates the asymptotic properties of quasi-maximum likelihood (QML) estimators for random-effects panel data transformation models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoskedasticity, and simple model structure. We develop a QML-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the QML estimators, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance (VC) matrix. Monte Carlo results reveal that the QML estimators perform well in finite samples, and that the gains by using the robust …
Boundary Limit Theory For Functional Local To Unity Regression, Anna Bykhovskaya, Peter C. B. Phillips
Boundary Limit Theory For Functional Local To Unity Regression, Anna Bykhovskaya, Peter C. B. Phillips
Research Collection School Of Economics
This article studies functional local unit root models (FLURs) in which the autoregressive coefficient may vary with time in the vicinity of unity. We extend conventional local to unity (LUR) models by allowing the localizing coefficient to be a function which characterizes departures from unity that may occur within the sample in both stationary and explosive directions. Such models enhance the flexibility of the LUR framework by including break point, trending, and multidirectional departures from unit autoregressive coefficients. We study the behavior of this model as the localizing function diverges, thereby determining the impact on the time series and on …
The Heterogeneous Effects Of The Minimum Wage On Employment Across States, Wuyi Wang, Peter C. B. Phillips, Liangjun Su
The Heterogeneous Effects Of The Minimum Wage On Employment Across States, Wuyi Wang, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
This paper studies the relationship between the minimum wage and the employment rate in the US using the framework of a panel structure model. The approach allows the minimum wage, along with some other controls, to have heterogeneous effects on employment across states which are classified into a group structure. The effects on employment are the same within each group but differ across different groups. The number of groups and the group membership of each state are both unknown a priori. The approach employs the C-Lasso technique, a recently developed classification method that consistently estimates group structure and leads to …
A Dynamic Network Perspective On The Latent Group Structure Of Cryptocurrencies, Li Guo, Yubo Tao, Wolfgang Karl Hardle
A Dynamic Network Perspective On The Latent Group Structure Of Cryptocurrencies, Li Guo, Yubo Tao, Wolfgang Karl Hardle
Research Collection School Of Economics
In this paper, we study the latent group structure in cryptocurrencies market by forming a dynamic return inferred network with coin attributions. We develop a dynamic covariate-assisted spectral clustering method to detect the communities in dynamic network framework and prove its uniform consistency along the horizons. Applying our new method, we show the return inferred network structure and coin attributions, including algorithms and proof types, jointly determine the market segmentation. Based on the network model, we propose a novel "hard-to-value" measure using the centrality scores. Further analysis reveals that the group with a lower centrality score exhibits stronger short-term return …
Determination Of Different Types Of Fixed Effects In Three-Dimensional Panels, Xun Lu, Ke Miao, Liangjun Su
Determination Of Different Types Of Fixed Effects In Three-Dimensional Panels, Xun Lu, Ke Miao, Liangjun Su
Research Collection School Of Economics
In this paper we propose a jackknife method to determine the type of fixed effects in three-dimensional panel data models. We show that with probability approaching 1, the method can select the correct type of fixed effects in the presence of only weak serial or cross-sectional dependence among the error terms. In the presence of strong serial correlation, we propose a modified jackknife method and justify its selection consistency. Monte Carlo simulations demonstrate the excellent finite sample performance of our method. Applications to two datasets in macroeconomics and international trade reveal the usefulness of our method.